Update app.py

app.py

@@ -11,9 +11,10 @@ os.environ["GRADIO_TEMP"] = tempfile.mkdtemp()
 # Load the saved Random Forest model
 rf_model = joblib.load('rf_model.pkl')  # Ensure the correct model path
 
-# Define numeric features (Now exactly 6)
+# Define required numeric features
 numeric_features = [
-    "date_numeric", "time_numeric", "door_state", "sphone_signal", "label"
+    "date_numeric", "total_minutes", "seconds",
+    "door_state", "sphone_signal", "label"
 ]
 
 # Class labels for attack types
@@ -35,7 +36,8 @@ def convert_datetime_features(log_data):
         log_data['date_numeric'] = log_data['date'].astype(np.int64) // 10**9  
 
         time_parsed = pd.to_datetime(log_data['time'], format='%H:%M:%S', errors='coerce')
-        log_data['time_numeric'] = time_parsed.dt.hour * 3600 + time_parsed.dt.minute * 60 + time_parsed.dt.second
+        log_data['total_minutes'] = (time_parsed.dt.hour * 60) + time_parsed.dt.minute
+        log_data['seconds'] = time_parsed.dt.second
     except Exception as e:
         return f"Error processing date/time: {str(e)}"
     
@@ -50,18 +52,14 @@ def detect_intrusion(file):
 
     log_data = convert_datetime_features(log_data)
 
-    # Ensure required features exist
     missing_features = [feature for feature in numeric_features if feature not in log_data.columns]
     if missing_features:
         return f"Missing features in file: {', '.join(missing_features)}"
 
     try:
-        # Convert categorical and numeric values
         log_data['door_state'] = log_data['door_state'].astype(str).str.strip().replace({'closed': 0, 'open': 1})
         log_data['sphone_signal'] = pd.to_numeric(log_data['sphone_signal'], errors='coerce')
-        log_data['label'] = pd.to_numeric(log_data['label'], errors='coerce')
 
-        # Extract only the required numeric features
         feature_values = log_data[numeric_features].astype(float).values
         predictions = rf_model.predict(feature_values)
     except Exception as e:
@@ -88,12 +86,14 @@ iface = gr.Interface(
     inputs=[gr.File(label="Upload Log File (CSV format)")],
     outputs=[gr.Dataframe(label="Intrusion Detection Results"), gr.File(label="Download Predictions CSV")],
     title="Intrusion Detection System",
-    description=("""
+    description=(
+        """
         Upload a CSV log file with the following features:
         date,time,door_state,sphone_signal,label
         Example:
-        26-04-19,13:59:20,1,-85,2
-    """)
+        26-04-19,13:59:20,1,-85,normal
+        """
+    )
 )
 
 iface.launch()